Label Annotation through Biodiversity Enhanced Learning

Heidorn, P. Bryan; Zhang, Qianjin

Permalink

https://hdl.handle.net/2142/42056 Copy

Description

Title

Label Annotation through Biodiversity Enhanced Learning

Author(s)

• Heidorn, P. Bryan
• Zhang, Qianjin

Contributor(s)

Chong, Steven

Issue Date

2013-02

Keyword(s)

• OCR
• parsing
• semantic markup
• digital curation and preservation
• information retrieval
• machine learning

Abstract

The LABELX (Label Annotation through Biodiversity Enhanced Learning) is an extension of the HERBIS NLP system reported previously (Heidorn & Wei, 2008). The objective of the system is to formaly structure output from Optical Character Recognition (OCR) of the highly variable labels of natural history museum specimens. OCR errors are common in the OCR output. Genus and species names are particularly prone to errors. Records are preprocessed using a fuzzy-match algorithm to find and replace genus and species names, including those with OCR errors, and replace those with a constant token. Integers and strings that begin with Alphabetic characters and end with numbers are also replaced with tokens. LABELX generates structured XML data and RDF and makes corrections to OCR errors in some fields. The main algorithm is a Hidden Markov Model (HMM). This poster reports an enhancement to the previous system with a larger data set.

Publisher

iSchools

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/42056

DOI

https://doi.org/10.9776/13450

Copyright and License Information

Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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